Every year, more than 2 million people in the UK are affected by cardiac arrhythmia (heart rhythm abnormalities) which can lead to stroke, cardiac arrest, or even sudden cardiac death. In particular, atrial fibrillation (AF) is responsible for 20% of all strokes caused by clots (ischemic stroke). The population of AF patients is around 1.5 million in the UK alone.
However, early detection allows the commencement of treatment which can allow patients to lead a normal life, and thus is of great importance. Yet, AF in early stages occurs sporadically and inconsistently in short episodes, termed “paroxysmal AF”, which may be difficult to detect in short tests. This is before developing into more sustained episodes, termed “persistent AF”. In these early stages, round-the-clock monitoring is necessary to capture these short episodes.
Existing solutions are adequate in detecting what is known as “clinical AF”, by operating on the order of minutes and diagnosing based on the fraction of time spent in AF and non-AF. This is to reduce false alarms. However, very short episodes of AF that may be “subclinical” during the paroxysmal stage may go undetected in such algorithms.
Turning to more details of AF, this is the most common type of cardiac arrhythmia, and is a condition of the heart whereby the atria—upper chambers of the heart—do not coordinate well to pump blood through the body. This may allow blood clots to form, which can lead to a stroke when they travel to the brain.
Having AF increases the risk of stroke in patients by 5 times [1], and the overall risk of death in patients by twice [2]. A stroke afflicts 100,000 people per year in England and Wales [3] (equivalent to one person every 5 minutes), and 20% of all strokes caused by such clots (known as an ischemic stroke) result from AF [4]. An estimated 1.5 million people in the UK have AF currently [2], and the NHS spends over £2.2 billion a year on treating AF and AF related illnesses [2]. By the time adults reach 40 years of age, they have a lifetime risk of about 25% of developing AF [5].
If AF is detected, patients may be put on treatment and medication like blood thinners (Warfarin in particular), which can reduce the risk of stroke by up to two thirds [6], and the risk of death by one third without significantly increasing the risk of major bleeding [2]. Stroke patients require a long recovery, and many suffer permanent neural damage. This has a significant impact on the workforce and economy, estimated to be around £2.4 billion per annum [2].
FIG. 1 illustrates a basic electrocardiograph (ECG) signal. This has several points which are labelled as P, Q, R, S, and T. These features arise from the electrical signals that pass through the different heart muscles in a procedural manner to allow the heart to pump blood normally. The voltage and time statistics—height, width, and time intervals of the various features—are key to diagnosing abnormalities in the heart rhythm. Most significantly, the P wave is the result from activity in the atria.
FIG. 2 illustrates a series of ECG signals which may be used for the detection of AF by doctors in clinics. They are, in order of reliability:                Irregularly irregular R-R intervals        Missing P waves        Presence of fibrillatory waves in the ECG base line.        
Using each indicator on its own has its setbacks, but work well when used altogether.
Irregular R-R intervals, while being the easiest to detect in most circumstances, may not indicate AF in some cases, as there are various other arrhythmia that also exhibit this.
Missing P waves are difficult to observe in cases where there are high noise levels which can obscure the baseline of the ECG signal, or if the ECG leads are not placed in positions to efficiently pick up electrical signals from the atria. There are also other arrhythmia that exhibit delayed, or advanced P waves, complicating the detection.
Fibrillatory waves on the ECG base line are the hardest to observe because they are irregular, and vary in amplitude from coarse to fine [7]. Thus they are easily obscured by noise and other interference such as electrical activity from muscles. Owing to this, fibrillatory waves are considered a “soft marker” for AF.
To make matters worse, AF occurs sporadically—termed “paroxysmal AF”—in patients in the early stage, before becoming continuous—termed “persistent AF”—in their later age. While in its early stage, a patient may only exhibit AF under specific physiological conditions, e.g. when patients are under physical stress, if they consume alcohol, etc, and these sporadic episodes of AF may occur for very short periods of time on the order of seconds. This means that for early detection, round-the-clock monitoring is needed so that there is the opportunity to capture these short episodes of AF.
Computer algorithms already exist for the detection of AF. The usual approach is to diagnose AF by a threshold of AF burden (i.e. percentage of beats which are AF in a certain time window), as seen in [8], to reduce false positives and diagnosis. This works well for the diagnosis of what is termed as “clinical AF”.
However, during the stage of paroxysmal AF, such episodes can be short enough that they can be passed over by such detection algorithms. These very short episodes are termed “subclinical AF”. According to a recent investigation [9], being diagnosed with subclinical AF places an individual at 5.5 times the risk of developing clinical AF, and 2.5 times the risk of stroke, both within a period of approximately 2.5 years. Early detection of AF can thus have significant impact, but requires acute accuracy in the algorithm, and at high resolutions.